Talent acquisition analytics Driving smarter sourcing and hiring decisions with data

Transcription

1 Talent acquisition analytics Driving smarter sourcing and hiring decisions with data

2 Organizations are recruiting talent in an increasingly complex and competitive talent acquisition (TA) environment one that requires greater market, candidate, and process intelligence. Quantitative approaches can provide that intelligence, enabling insights critical to improving operational and business outcomes, much as they do in marketing, sales, supply chain, and finance. From better awareness of candidate fit to process and technology optimization to increased understanding of TA s impact on the business, analytics is a powerful force in the quest to source and hire top talent. The challenge Employers have more pressure than ever to fill new jobs efficiently and effectively in the midst of low unemployment and skilled labor shortages. The challenges multiply with the increased risk of talent movement 44 percent of Millennial workers will be looking for a new job in two years, 1 and are 25 percent more likely to search for a new job than non-millennials. 2 With average new-hire turnover rates of 14 percent, 3 a clear need exists for improved projections and insights into staffing needs and strategies. Our research has found that 83 percent of 924 companies surveyed across the globe have low people analytics maturity, 4 focusing primarily on basic HR reporting needs, data security and privacy, etc., as compared to the high-maturity organizations, which have graduated to more advanced practices. The high-maturity organizations build consistent data definitions, use embedded reporting and analytics tools, and are building data integration capabilities to understand employee behaviors. This indicates that a majority of organizations are underequipped to identify problem areas and potential solutions to their recruiting challenges. Addressing this capability gap is a business imperative, given the need to effectively manage staffing and meet operational goals. The opportunity: Why enhanced TA analytics matter The impact of an advanced, high-maturity people analytics capability on business results is substantial, and enhancing it can increase the value of the TA and HR to the business (see figure 1). The first step to closing the capability gap and increasing analytics maturity is to understand how TA analytics affects key stakeholders and what both they and the organization can gain from it. You should consider the outcomes stakeholders are driving toward and the types of insights that will support achieving those outcomes. For example: 1

3 Talent acquisition analytics: Driving smarter sourcing and hiring decisions with data Figure 1. Financial outcomes comparison between high- and low-maturity people analytics organizations Financial performance indicator High-maturity organizations Low-maturity organizations Difference in performance, high-maturity organizations versus low-maturity organizations Three-year average revenue $46,948,812,000 $23,947,268,000 96% Three-year average cash flow from operations $5,916,299 $3,138,636 88% Three-year average gross margin (or profit) $17,117,698 $9,409,384 82% Source: High-impact people analytics industry study, Bersin, 2017, p. 45. The first step to closing the capability gap and increasing analytics maturity is to understand how TA analytics affect key stakeholders and what both they and the organization can gain from it. You should consider the outcomes stakeholders are driving toward and the types of insights that wil support achieving those outcomes (see figure 2). Figure 2. TA analytics stakeholder TA analytics use Desired TA analytics outcomes/impacts Candidates Analysis of process efficiency and the desired candidate experience Enhance candidate satisfaction, acceptance rates, and time and cost savings to the business Hiring managers and recruiters Process and predictive analytics Provide credible insights for better informed attraction, candidate review, and hiring discussions and decisions Contractors Process and predictive analytics Identify skill gaps and short-term requirements for staff augmentation, and address process inefficiencies in how you source, onboard, and track the contingent workforce TA leaders Advanced analytics and benchmarking Uncover trends that identify process and outcome issues, strengths to leverage, effective and ineffective sourcing options, and proactive improvements to strategies Business and HR leaders Advanced analytics Drive business planning and reveal the business impact of TA strategies on talent and business outcomes Considering the who and how of TA analytics impact not only helps stakeholders appreciate that advancing TA analytics capability can bring to the organization but also helps guide your analytics approach and develop a roadmap for the journey. 2

4 Talent acquisition analytics: Driving smarter sourcing and hiring decisions with data Defining leading TA analytics: A framework TA analytics refers to the systematic discovery of meaningful patterns in data to support decision making related to recruitment and onboarding processes, activities, and outcomes. Three primary categories of measurement (efficiency, effectiveness, and impact) are leveraged across four types of analysis (descriptive, relative, analytic, and predictive). As the framework progresses from left to right (see figure 3), the model increases in complexity of data used, systems tapped, and the sophistication of analytics techniques used. A robust infrastructure enables the analyses, with multiple data sources, repositories, analytic tools, staff capabilities, and visualization all focused on the business and information needs of the end users. Figure 3. Business, HR, TA strategies Efficiency Resource utilization, process outcomes Effectiveness Talent outcomes Impact Business outcomes Analysis spectrum Framework Infrastructure Internal TA data Volume/time/cost and source Hiring volume, vacancies Time to fill Source of hires External benchmarks Descriptive External Internal Talent, Relative Analytic TA data HR data business Predictive data What is occurring? How much/many/long? From where? Reporting system and governance End user requirements Key analytics Quality of candidates and hires Key measures Quality of candidate Quality of hire Candidate pool depth New hire performance, retention Speed to competency How well are we performing? Which tactics work best? How do we compare to others? ATS/CRM/HCMS/ERP/Warehouse Dashboard/visualization What is the relationship? Between activity and outcomes? Between cost and effectiveness? TA influence on business outcomes Critical role hiring Staffing vs. requirements Net hires Workforce requirements planning TA: business results Analytics team staffing Statistical analysis tools What will the future look like? What tactics most influence TA, business outcomes? 3

5 Analytic approaches Talent acquisition analytics: Driving smarter sourcing and hiring decisions with data Each type of analysis offers valuable insights toward the overall success of the TA function and is progressively more sophisticated in its nature. Descriptive analysis Relative analysis Provides a view into activity, such as requisition volume, applicant or talent pool size, source of hires, etc. It reveals the levels of activity and efficiency in candidate generation and is a simple expression of volume, time, cost, or source. Offers insights into TA performance against requirements or standards, including cost-per-hire, time-to-fill, pre-hire assessment scores, etc. It compares performance to budgets, tests, SLAs, or benchmarks; is represented as ratios or comparisons; and is critical to the efficient operation of the TA function. Analytic analysis Predictive analysis Answers questions regarding the relationship between activities and outcomes, as in the quality of candidates or hires, skill match between candidates and position requirements, critical-skill hire retention, speed to competency, etc. It uses basic statistics and multiple datasets to map TA activity to subsequent talent outcomes. Identifies statistical relationships between multiple activities and outcomes to either (1) predict what will happen in the future or to explain the drivers of that outcome, such as a candidate s likely cultural fit, level of performance, and retention, or (2) detect potential talent shortages/skills gaps and market availability (workforce planning). Predictive techniques also identify possible adjustments to TA strategies and the opportunity to deploy automation and/ or contingent workforce solutions. These techniques leverage advanced statistical and modeling techniques with large, integrated datasets. Leading organizations measure and report on all four metrics across the analysis spectrum, presenting reports and dashboards specific to the information needs of each end user group. 4

6 A key consideration: Sources of data Leading companies no longer rely exclusively on the applicant tracking system (ATS) for reporting on transactional data. With the adoption of more advanced systems, data repositories, and analytics capabilities, the aperture expands to provide more integrated insights into the impact of TA processes and practices, including data from: Core HR, talent, learning, and performance management and compensation systems for new-hire and high-performer demographic information and their influence on talent outcomes (performance, employee movement, speed to competency, high potential status, etc.) Skills inventories to identify skill surpluses and shortfalls within roles Candidate relationship management (CRM) systems for talent pool Social networks for passive candidate and employment brand strength insights Employee engagement surveys for evaluating new-hire satisfaction, retention risk, and hiring manager success Operational and financial systems (e.g., Sales, ERP) for recruiting costs, and the impact of hiring activity on team, business unit, and/ or corporate results Data from these systems also shed light on the relative talent management value of sources of hire both internal and external for generating high performers, highly engaged employees, or future leaders within a critical role, line of business, geography, or diversity group. Case in point A global financial services organization sought an analytical approach to ensure that its future customer service workforce would be successful in a new organization model. A robust pre hire analytics model was developed to predict employee success on the job. More than 6 million records were amassed using 12 internal and external data sources that spanned 70+ disparate files and contained 100+ data elements. The model validated that representatives in the highest predicted success group realized a 45 percent actual success rate versus 8 percent in the lowest predicted success group. 5

7 Where to apply analytics for high impact and value Talent acquisition analytics: Driving smarter sourcing and hiring decisions with data High-impact, high-value applications of TA analytics focus on desired talent and business outcomes and provide actionable results. These enable robust decision support related to candidate selection, process design, budget/resource investments, and TA activity s contribution to business objectives (see figure 4). Figure 4. Use case Types of data/measures Business value Quality of candidate and hire prediction Process of optimization assessment Business impact Pre-hire assessments Performance evaluations and feedback speed to competency retention/turnover risk Cultural fit Time to fill (by process step) Digital experience Cost of quality hires per source Staffing to budget/workforce plan TA outcomes impact on business results University/diversity/veteran hiring against goals Internal mobility impact on retention, replacement timeliness, and business outcomes Understanding and leveraging the demographics and drivers of short- and long-term job success Evaluating the quality and depth of talent pools Looking beyond the candidates presentation to more objectively understand their motivations and workstyles Assessing processes that increase time to process/close qualified candidates Identifying where in the digital experience that desired outcomes begin to diminish, and by how much Understanding the cost/benefit of candidate sourcing tactics for tactical adjustments Assessing TA goals vs. organizational objectives Understanding the relative influence various TA processes and practices have on business goals/objectives Identifying the success and impact of targeted recruitment programs Evaluating the drivers of the successful onboarding and retention of new hires Assessing the relative value of external recruiting vs. internal development and succession strategies 6

8 The future of TA analytics New advancements in cognitive technology are gaining a foothold in TA, creating opportunities for increased efficiency, accuracy, and insights. While these are advancing rapidly, the most ready-now solutions exist in candidate sourcing and screening and can potentially increase newhire quality and reduce the impact of selection biases. Emerging capabilities in data mining and pattern recognition enable best-fit recommendations to candidates for open roles, 6 and can better identify the combinations of top-candidate social and technical/professional media viewing patterns for enhanced targeting. Machine-learning methodologies also hold the promise of continuously updating (statistical) validation of candidate assessments, enabling more efficient and responsive evaluations of the accuracy of pre-hire, retention, and other types of predictions (see figure 5). Figure 5. Artificial intelligence Talent analytics, metrics, and insights to drive decision making Pattern recognition Works closely with data mining to identify sequences and logical grouping in data Candidate scoring against desirable skills and competencies Resume screening and candidate experience Cognitive insights Data mining Powered by machine learning to evolve with time and experiences to manage unstructured data Smart chatbots Interactions with users to achieve specific objectives Automated job and candidate matching Interview bots for initial candidate assessment Robotic process automation Routine, standardized, manually intensive, high-volume tasks Cognitive automation Cognitive engagement Semantic computing Text analytics, computer vision and voice recognition to understand semantics Automation of manual license and document verification processes Special media/portfolio scans for candidate screening 7

9 Talent acquisition analytics: Driving smarter sourcing and hiring decisions with data Getting started: How organizations can begin to upgrade As noted above, moving beyond traditional analytics into an expanded range of insights starts with understanding the needs of your business and stakeholders a valuable opportunity to improve their experiences and outcomes. To understand the most useful TA analytics, we recommend focusing on: Business strategies: What are the primary business goals and associated talent outcomes required to meet business strategy? Decision support: What insights will best support talent and business decision making by leaders, hiring managers, and recruiters, each of whom has unique information needs? Moments that matter : What process steps differentiate the experience for critical-skill candidates and hiring managers? These moments then become process and outcome measurement targets. In addition, include these infrastructure considerations when designing your delivery of new analytics: Data source integration: What types of data are available, such as ATS, CRM, HRMS, and ERP data? What tools are available/needed to compile and analyze the data? Visualization: What is your ability to present and deliver timely, consistent, and easy-to-digest reports and dashboards? Analytic capabilities: What are the current analytic skill levels of recruiters, hiring managers, HR business partners, and business leaders to adopt and use the insights? Answering these questions gives you a gauge of where you stand, where strategic opportunities lie, and what infrastructure components to target so you can begin to apply TA analytics and derive tangible insights and business value from your existing data. 8

10 Endnotes Deloitte Millennial Survey: Apprehensive Millennials seeking stability and opportunities in an uncertain world, Deloitte Development LLC, 2017, www2.deloitte.com/ch/en/pages/human-capital/articles/millennialsurvey.html. 2. Amy Adkins, Millennials: The job-hopping generation, Gallup, 2016, 3. Deloitte Consulting LLP, Bersin interactive factbook (talent acquisition benchmarks listing), Bersin, 2015, bersin.com/dashboard, p. 47, Madhura Chakrabarti and R. J. Milnor, High-impact people analytics: The recalculated route to maturity, Deloitte Development LLC, 2017, p Wendy Wang-Audia, Talent analytics: From small data to big data (research bulletin), Deloitte Development LLC, January 25, 2013, 6. Michael Stephan, Say hello to the cognitive recruiter, Wall Street Journal, April 12, 2017, say-hello-to-the-cognitive-recruiter/. Contacts Art Mazor Principal Deloitte Consulting LLP Kevin Moss Managing Director Deloitte Consulting LLP Charles Goretsky Manager Deloitte Consulting LLP Dave Fineman Specialist Leader Deloitte Consulting LLP Bill Cleary Senior Manager Deloitte Consulting LLP

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